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Autoencoder and Evolutionary Algorithm for Level Generation in Lode Runner Sarjak Thakkar, Changxing Cao, Lifan Wang, Tae Jong Choi, Julian Togelius New York University New York, USA ftsarjak, cc5766, lw2435, tc3045, [email protected] Abstract— Procedural content generation can be used to Runner. In addition, we hse a multi-population evo- create arbitrarily large amounts of game levels automati- lutionary algorithm that contains patch-wise crossover cally, but traditionally the PCG algorithms needed to be and latent variable mutation operators, which can re- developed or adapted for each game manually. Procedural Content Generation via Machine Learning (PCGML) har- tain some interesting features of original levels while nesses the power of machine learning to semi-automate the inserting less predictable variations. Our experimental development of PCG solutions, training on existing game results showed that the proposed method is able to content so as to create new content from the trained models. create diverse levels. Besides, we found out that vari- One of the machine learning techniques that have been ational autoencoders prone to generate more complex suggested for this purpose is the autoencoder. However, very limited work has been done to explore the potential levels than vanilla autoencoders. of autoencoders for PCGML. In this paper, we train au- toencoders on levels for the platform game Lode Runner, II. RELATED WORK and use them to generate levels. Compared to previous In the past, Summerville et al. used LSTMs on the work, we use a multi-channel approach to represent content in full fidelity, and we compare standard and variational string representation of the map for PCG [3]. Snodgrass autoencoders. We also evolve the values of the hidden layer et al. suggested learning statistical patterns from exist- of trained autoencoders in order to find levels with desired ing levels generated by humans, and use those patterns properties. for training Markov Chains and for generating new levels from such Markov Chains [4]. In 2016, Font et al. I. INTRODUCTION suggested using evolutionary methods for generating levels by using two-phase level encoding [5]. In 2017, Procedural content generation (PCG) techniques are Samuel Alvernaz et al. trained an autoencoder to create used for game content for several reasons, including a comparatively low-dimensional representation of the introducing variation to the player, enabling particular environment observation, and then use CMA-ES to game aesthetics, and automating some parts of game train neural network controllers acting on this input development. Most PCG algorithms need to be devel- data [6]. The bottom line of all these approaches is that oped or tailored to each game. In contrast, PCGML uses different representations of the same game combined machine learning to generate content, using models with different algorithms achieve varying results. trained on existing content [1]. On the other hand, Lode Runner has a much larger One machine learning method that has only been variety of chunks, and Lode Runner maps rely heavily cursorily investigated for PCGML is the autoencoder, on puzzle-like structures and mazes in the level de- a type of neural network used for feature compres- sign [7]. However, our approach of using variational sion. It was recently proposed to use autoencoders autoencoder to learn the complex underlying structure for tasks such as level generation, level repairing, and of Lode Runner levels, coupled with an evolutionary level recognition [2]. However, that study really only algorithm to increase the playability score of the levels, scratched the surface. Among other things, the autoen- generated good results. Notably, we find most of the coder in that study using a single channel for all types raw generated levels from autoencoder as not playable of tiles, severely limiting the usefulness of the method. until the evolution step is carried out. We will show In this paper, we build on the recent research on more details of the technique we used in the following using autoencoders for PCG [2]. We propose a multi- sections. channel encoding for autoencoders to learn the struc- ture and properties of the levels of Lode Runner. III. OUR APPROACH Concretely, the proposed method uses a five channel We selected a game called Lode Runner in order encoding to separately learn the patterns of the bricks, to evaluate our approach. We used the multi-channel ladders, ropes, enemies, golds of the levels of Lode encoding for the 150 original levels and trained an autoencoder using that data. The autoencoder is used 978-1-7281-1884-0/19/$31.00 c 2019 IEEE to generate a pool of 10,000 levels by inputting ran- Parent Level 1 Parent Level 2 dom seeds to the decoder. We evolved these levels by XXX OOO passing them through the evolutionary process. XXX OOO XXX OOO A. Multi-Channel Encoding Crossover While selecting the format of level data to be used XXX for an autoencoder, the primary goal was to make the XXX computation for encoding and decoding more logically XOO feasible and reduce as much complexity as possible. In order to achieve this, we divided each level into various blocks that represent entities such as bricks, Mutate Variational Latent Space Variational ladders, ropes, etc. By doing so, we reduced each level Encoder Decoder to a small size of 22x32 blocks. This representation can be considered as a 2D string, where each character of the string represents a block of a level (Fig. 1). For example, we used ’b’ for a brick and ’-’ for a rope. The Second Generation Level string representation enables our autoencoder to learn the underlying structure of a level. Fig. 3: Evolutionary Process of Proposed Method Each character of a string is then later converted to a binary encoding for separate channels. In other words, Diederik et al. showed that the posterior inference can the proposed method uses separate channels for bricks, be made especially efficient by fitting an approximate ladders, ropes, etc. Each channel has 1 at the position inference mode [8]. The variational autoencoder is more depending on whether the particular block exists in advanced as it creates a distribution for each latent a level, and 0 otherwise. This representation can be variable [9]. Hence, the variational autoencoder is able compared to the RGB channels of a color image. We to reproduce images with more variations, compared to used five channels for the proposed method. Fig. 2 the vanilla autoencoder. We employed the variational shows a brick and ladder encodings of a level. autoencoder in the proposed method. One is to produce a large dataset that is up to 10,000 images as a global gene pool before the evolutionary process; the other is used in the evolutionary process to perform mutation on the latent representation of the level. C. Evolutionary Process After creating a global gene pool, the proposed method uses the evolutionary operators such as crossover and mutation to generate playable levels. For the level generation in Lode Runner, the proposed method uses the specially designed crossover and mu- Fig. 1: Example of String Representation of Level tation operators, fitness function, and multi-population structure, which will be explained in the following subsections. 1) Crossover and Mutation Operators: In order to retain some interesting features of original levels, we applied a patch-wise crossover. As illustrated in Fig. 3, the parent images are divided into nine patches, and a child image inherits the first seven patches from parent 1 and the last two from parent 2. For the mutation, the proposed method takes the child image, passes it through the autoencoder, and randomly mutate its Fig. 2: Bricks (Red) and Ladders (Green) Encoding latent variable. The parameters for the crossover and mutation operators can be found in Table I. Fig. 3 illustrates the process of the mutation operator. B. Variational Autoencoder 2) Fitness Function: This section describes the fitness Autoencoders have been used a variety of fields function of the proposed method, which is consists of such as images, sounds, music. The representation playability and connectivity. of any data in the latent variable of an autoencoder • Playability: We employed a path-finding algorithm can be used to reconstruct the original data from it. (PFA) to check if all the golds of a generated level TABLE I: Used Control Parameters a brick, it keeps falling until it reaches a rope, ladder or brick. We employed A* algorithm as a PFA of the Crossover Mutation Population Number of Rate Rate Size Generations proposed method to find the shortest path for collecting 0.78 0.1 32 150 all golds. Fig 5 is the solution path of an original level from A* algorithm. An extension of this approach, where the PFA tries to reach every block of the level helps to determine the connectivity of the level. can be collected. If they can be collected, the level is playable; otherwise, not playable. • Connectivity: The connectivity measure evaluates the percentage of coordinate points in a generated level that can be reached. The proposed method checks the playability measure first, and only playable levels are chosen to evolve. After that, the proposed method applies the connec- tivity measure in order to evaluate the fitness of each playable level. In other words, the more blocks are Fig. 5: Shortest Path from A* Algorithm connected, the higher fitness scores are returned. We employed a PFA to check what proportion of the coordinate in a level is reachable. IV. RESULTS 3) Multi-Population Structure: Since we faced the A. Results of Proposed Method with Variational Autoen- lack of variations in generated levels from one evo- coder lution iteration, we applied a multi-population struc- Fig. 6(a) consists of four randomly chosen levels ture to avoid the premature convergence.

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